Aging, social networks, organizations.
I have always been intrigued by theoretical and empirical puzzles that evolve around social networks and well-being. Specifically, my main interest lies in the study of the dynamic link between social relationships, individual behavior and health outcomes. Many scholars in social epidemiology have emphasized the positive and negative effects of integration into social networks on health and longevity. Yet, the mechanisms underlying the impact of social networks on health have not fully been unraveled.
My two key foci comprise of the dynamics of informal networks on the one hand, and the positive and negative outcomes of personal networks on the other hand. In my research, I apply the latest techniques for the cross-sectional and longitudinal analyses of complete social networks and ego-networks.
Personal networks of older adults
After completion of my PhD research, I broadened my focus towards the field of healthy aging. I am interested in how integration into social networks may promote or hamper well-being in late adulthood. This contained, for example, comparative research on social contacts of older adults across 27 European countries, using data from the European Social Survey (ESS) and Eurostat.
Furthermore, I investigated the influence of characteristics of ego-networks—such as social support, loneliness, relationship diversity and grandparenthood—on cognitive functioning and mortality of older adults. These studies used unique data from the Longitudinal Aging Study Amsterdam (LASA) and the Health and Retirement Study (HRS). Analytical strategies mainly employed growth curve modeling, fixed-effects modeling and survival analysis. Results show that older adults embedded into personal networks of supportive and diverse relationships more often enjoy good cognitive health and live longer than their counterparts with less supportive and rather homogeneous networks.
Informal networks in organizations
In my PhD research, I studied gossip and trust in organizations using social network analysis. The analytical approach included mostly exponential random graph modeling (ERGM) and stochastic actor-oriented models (RSiena).
Gossip, broadly defined as talking about absent third parties, is typically believed to have detrimental effects on the employees’ well-being. In contrast to previous research that perceived gossip as individual behavior, my research shows that gossip is better understood as interactions between multiple interdependent actors. For this project, I collected complete social network data on gossip and interpersonal trust in a Dutch organization together with colleagues over a period of approximately two years.
My findings revealed that gossip behavior mainly depends on the employees’ trust and distrust in both their colleagues and their managers: Those who trust their colleagues but distrust their managers are most likely to spread negative information about others. Another crucial result is that there seems to be a natural regulation of extensive gossiping, as engaging in much gossip meant losing friends in the organization in the long run.